Task Adaptation in Industrial Human-Robot Interaction: Leveraging Riemannian Motion Policies
METADATA ONLY
Loading...
Author / Producer
Date
2024-07
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
In real-world industrial environments, modern robots often rely on human operators for crucial decision-making and mission synthesis from individual tasks. Effective and safe collaboration between humans and robots requires systems that can adjust their motion to human intentions, enabling dynamic task planning and adaptation. Addressing the needs of industrial applications, we propose a motion control framework that (i) removes the need for manual control of the robot’s movement; (ii) facilitates the formulation and combination of complex tasks; and (iii) allows the seamless integration of human intent recognition and robot motion planning. For this purpose, we leverage a modular and purely reactive approach for task parametrization and motion generation, embodied by Riemannian Motion Policies. The effectiveness of our method is demonstrated, evaluated and compared to a representative state-of-the-art approach in experimental scenarios, inspired by realistic industrial Human-Robot Interaction settings.
Permanent link
Publication status
published
External links
Book title
Robotics: Science and System XX
Journal / series
Volume
Pages / Article No.
26
Publisher
Robotics Science & Systems Foundation
Event
Robotics: Science and Systems Conference (RSS 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.